inputs-are-not-modified.https.any.js

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// META: title=test that input tensors are not modified during a call to dispatch()

// META: global=window,dedicatedworker

// META: variant=?cpu

// META: variant=?gpu

// META: variant=?npu

// META: script=../resources/utils.js

// META: timeout=long

'use strict';

// https://www.w3.org/TR/webnn/#api-mlcontext-dispatch

let mlContext;

// Skip tests if WebNN is unimplemented.

promise_setup(async () => {

  assert_implements(navigator.ml, 'missing navigator.ml');

  mlContext = await navigator.ml.createContext(contextOptions);

});

promise_test(async () => {

  const builder = new MLGraphBuilder(mlContext);

  const inputOperand =

      builder.input('input', {dataType: 'float32', shape: [4]});

  const hardSwishOperand = builder.hardSwish(inputOperand);

  // Add some other operator for the output tensor to bind to; otherwise there

  // is no reason to implement hardSwish "in-place".

  const outputOperand = builder.identity(hardSwishOperand);

  const [inputTensor, outputTensor, mlGraph] = await Promise.all([

    mlContext.createTensor({

      dataType: 'float32',

      shape: [4],

      readable: true,

      writable: true,

}),

    mlContext.createTensor({dataType: 'float32', shape: [4], readable: true}),

    builder.build({'output': outputOperand})

]);

  const inputData = Float32Array.from([-4, -1, 1, 4]);

  mlContext.writeTensor(inputTensor, inputData);

  mlContext.dispatch(mlGraph, {'input': inputTensor}, {'output': outputTensor});

  // Wait for graph execution to complete.

  await mlContext.readTensor(outputTensor);

  // The input tensor should not be modified.

  assert_array_equals(

      new Float32Array(await mlContext.readTensor(inputTensor)), inputData);

}, 'input tensor is not modified: hardSwish');

promise_test(async () => {

  const builder = new MLGraphBuilder(mlContext);

  const inputOperand =

      builder.input('input', {dataType: 'float32', shape: [4]});

  const constantOperand = builder.constant(

      {dataType: 'float32', shape: [4]}, Float32Array.from([-2, 0, 3, 4]));

  const mulOperand = builder.mul(inputOperand, constantOperand);

  // Add some other operator for the output tensor to bind to; otherwise there

  // is no reason to implement mul "in-place".

  const outputOperand = builder.add(mulOperand, constantOperand);

  const [inputTensor, outputTensor, mlGraph] = await Promise.all([

    mlContext.createTensor({

      dataType: 'float32',

      shape: [4],

      readable: true,

      writable: true,

}),

    mlContext.createTensor({dataType: 'float32', shape: [4], readable: true}),

    builder.build({'output': outputOperand})

]);

  const inputData = Float32Array.from([1, 2, 3, 4]);

  mlContext.writeTensor(inputTensor, inputData);

  mlContext.dispatch(mlGraph, {'input': inputTensor}, {'output': outputTensor});

  // Wait for graph execution to complete.

  await mlContext.readTensor(outputTensor);

  // The input tensor should not be modified.

  assert_array_equals(

      new Float32Array(await mlContext.readTensor(inputTensor)), inputData);

}, 'input tensor is not modified: mul');